In her first guest post for WhichPLM, Javeria Gauhar Khan of Data Ladder upholds the importance of clean data. When it comes to the implementation of any business-wide system or process – PLM, ERP, AI – the benefits can only be realized if the data you put in is of ample quality. Data Ladder is a renowned data matching solution.
Many organizations strive to make processes and transactions data-driven. Yet, be that as it may, data quality is still a major challenge for organizations, causing them to face consequences down the line. To become data driven, I believe companies need to utilize data cleaning solutions for disposal of undesirable, inaccurate and redundant data that doesn’t adversely affect an organization’s goals.
Meaningless data can cause a lot of menace. If your system is plagued with inaccurate, invalid, redundant data, that’s a red signal and indicates that your system is in a dire need of solutions for some serious underlying problems.
What is data quality?
Data quality indicates the overall health of your data, generally considering data integrity, accuracy, cleanliness, and several other factors that ultimately decides the fate of your organization or business. Utilizing your data to its fullest benefit depends on the capability of a company to provide clean data to employees to make important business decisions.
Poor quality of data can raise a significant amount of difficulties in your business; for instance it can lead to confused decision-making, and flawed statistical reporting which can have a drastic impact on your business outcomes and customer relationships.
To successfully eliminate the menace, companies can utilise a pool of data engineers and scientists to analyze, merge, refine and dedupe your data. Many organizations have shown up with numerous advancements in this regard, and many others became the dominant focal point of the AI world with deep analytics and implementation of solutions.
Why poor data quality can become a barrier?
Poor data quality isn’t just an ordinary issue. Millions of small and large firms transfer and store data in huge chunks through a range of small and large connected systems and logs. Some firms are operating in multiple departments, which requires a huge influx of data.
Here are some of the reasons why poor data can be a hindrance to your business goals:
Unorganized data sources
Most of the time organizations store and retrieve information from multiple sources, and maintain several connected systems for data management. There is a huge possibility that multiple sources store redundant information; for instance a customer care service and advertising record data on separate sources to store details of a user, there’s a chance that multiple sources suffer from data redundancy, and mixed information that might have a grave impact over reports and decisions made.
Sabotage your services
Quality data plays a crucial role in stabilizing your businesses interactions and activities while poor data does the exact opposite. For instance, many organizations send update notifications to the users subscribed to a specific topic, but inaccurate data may target the wrong audience, or fail to target at all (in case you have the wrong details of a person stored on your database). The marketing department is the heart of your business and demands regular checking of data quality. Social media and email campaigns, advertisement or any other activity mainly relies on customer data – even minor mismanagement in the data can inflict a catastrophe.
Dire impact on your sales
Sales records are equally important for an organization along with the consumer details, as it helps the organization track investments, earnings, profits, and so on. Usually, such departments have implemented strict contingency plans and dedupe solutions, but negligence can negatively impact statistical reporting and the flow of your business.
Why is there a need for data quality in the AI world?
Artificial intelligence is emerging as a core contributor to the business and analytic competency. AI isn’t new to us, and most of us are probably aware of its basic benefits, so what exactly does AI have to do with businesses and data quality? Well, AI can help in analysis and building valuable marketing strategies, more accurate engagement with the audience and give a better understanding of their preferences.
The potential of AI knows no bounds; several popular studies have shown that most businesses and startups are focusing on AI-based operations and services to serve the masses. The benefit goes both ways – to the organizations, as well as consumers believe that AI significantly enhances customer relationships.
So, how can I get started?
Whatever tools or algorithms you’re going to apply are going to be as good as your data is. If the data you’re feeding isn’t accurate in the first place, the outcome would barely make any difference for your organization. For this you can also make use of data cleansing tools to let users easily clean their data across data sets. It includes the data cleansing process, which cleans data from typos, spelling errors, character issues, punctuation issues, and the minor details that human data operators easily miss. This indicates the role of quality data in AI, so make sure you kick-start your journey with clean data.
Strategies to start with AI
Make a roadmap and know your expectations and objectives clearly.
Get the best team to execute your plans into reality. Operating AI-related tools requires exceptional skills and expertise, so be wise and patient in your choice.
Having the understanding of your customers is more important than only focusing towards implementing algorithms. So make sure you keep a record of their choices and improve their choices based on your research.
AI-based systems require maintenance and updates from time to time. The process goes through constant learning and implementation of data and algorithms. Make sure your data is in safe hands while going through important updates and changes.